Improvement and verification of urban extreme Temperature predictions with satellite and ground observations in Austria.

Extreme temperatures, especially long-lasting heat and cold waves in urban areas, lead to thermal stress of the population and increase the number of weather-related health risks and deaths. The observed climate trend and the associated increase of extreme weather events are expected to continue in the future. Thus, the evaluation of urban thermal stress and the associated health effects becomes an important issue for urban planning and risk management. To minimize the potential dangers of extreme temperatures, accurate weather forecasts are particularly important. For Austrian cities, an information system for temperature warnings already exists, which is based on the information of regional weather forecast models. However, this information does not have the required spatial resolution needed to resolve urban structure and thus to account for the urban heat island effect.

This project intends to design, set up and test a probabilistic Numerical Weather Prediction model system that is able to provide extreme temperature forecasts for urban regions in Austria with a hitherto unavailable spatial resolution. The base for this is given through a combined NWP model system of meso and microscale models, satellite and ground observations such as crowd-sourcing data. Satellite land surface data will be refined using machine learning techniques and used to improve the current land cover parameterisation and to verify the model’s temperature outputs during extreme weather events. Finally, hourly temperature and thermal comfort maps will be generated serving as base for future prediction and warning systems.